Development and Evaluation of the Algorithm CErtaInty Tool (ACE-IT) to Assess Electronic Medical Record and Claims-based Algorithms’ Fit for Purpose for Safety Outcomes

Abstract

Introduction Electronic health record (EHR) or medical claims-based algorithms (i.e., operational definitions) can be used to define safety outcomes using real-world data. However, existing tools do not allow researchers and decision-makers to adequately appraise whether a particular algorithm is fit for purpose (FFP) to support regulatory decisions on drug safety surveillance. Our objective was to develop a tool to enable regulatory decision-makers and other stakeholders to appraise whether a given algorithm is FFP for a specific decision context. Methods We drafted a set of 77 generic items informed by regulatory guidance documents, existing instruments, and publications. The outcome of ischemic stroke served as an exemplar to inform the development of draft items. The items were designed to be outcome independent. We conducted a three-round online Delphi panel to develop and refine the tool and achieve consensus on items (> 70% agreement) among panel participants composed of regulators, researchers from pharmaceutical organizations, academic clinicians, methodologists, pharmacoepidemiologists, and cardiologists. We conducted a qualitative analysis of panel responses. Five pairs of reviewers independently evaluated two ischemic stroke algorithm validation studies to test its application. We developed a user guide, with explanation and elaboration for each item, guidance on essential and additional elements for user responses, and an illustrative example of a complete assessment. Furthermore, we conducted a 2-h online stakeholder panel of 16 participants from regulatory agencies, academic institutions, and industry. We solicited input on key factors for an FFP assessment, their general reaction to the Algorithm CErtaInty Tool (ACE-IT), limitations of the tool, and its potential use. ResultsThe expert panel reviewed and made changes to the initial list of 77 items. The panel achieved consensus on 38 items, and the final version of the ACE-IT includes 34 items after removal of duplicate items. Applying the tool to two ischemic stroke algorithms demonstrated challenges in its application and identified shared concepts addressed by more than one item. The ACE-IT was viewed positively by the majority of stakeholders. They identified that the tool could serve as an educational resource as well as an information-sharing platform. The time required to complete the assessment was identified as an important limitation. We consolidated items with shared concepts and added a preliminary screen section and a summary assessment box based on their input. The final version of the ACE-IT is a 34-item tool for assessing whether algorithm validation studies on safety outcomes are FFP. It comprises the domains of internal validity (24 items), external validity (seven items), and ethical conduct and reporting of the validation study (three items). The internal validity domain includes sections on objectives, data sources, population, outcomes, design and setting, statistical methods, reference standard, accuracy, and strengths and l...

Development and Validation of Coding Algorithms to Identify Patients with Incident Non-Small Cell Lung Cancer in United States Healthcare Claims Data

“…The fit for purpose of any RWE algorithm is dependent on its context of use 1 and both internal validity (eg, algorithm performance within this study) and external validity (eg, generalizability of the algorithm to the real-world data source where it will be applied for conducting future research) should be assessed by researchers considering whether to use the algorithm. 26 In our view, based on the performance of the final point-score in terms of its sensitivity and PPV, the final point-score NSCLC algorithm is likely fit for purpose for general disease state studies including burden of illness and treatment patterns. Researchers should consider the algorithm’s performance in the context of their study question and data source, as described in the Certainty Framework for real-world data variables.…”